code: def forward(self, x):
      # Get 2 events.
      self.e1 = Event(enable_timing=True)
      self.e2 = Event(enable_timing=True)
  
      # Record the fake forward compute time.
      self.e1.record()
      if self.sleep_cycles > 0:
          torch.cuda._sleep(self.sleep_cycles)
      if self.optional_param is not None:
          x = (
              x + self.optional_param
          )  # force the param to be part of the graph
      self.e2.record()
      return x
  
imports: import sys
  import time
  from statistics import mean
  from unittest.mock import patch
  import torch
  import torch.nn
  from torch import distributed
  from torch.cuda import Event
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import FSDPTest
  from torch.testing._internal.common_utils import get_cycles_per_ms, run_tests, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 2 for calling the Layer obj's underlying member method code with example usage: obj.forward(x) and returns: x